Load testing, perhaps more than any other form of testing, is one of those activities that you either choose to do well or risk a result that leaves you worse off than not doing it at all. Half-hearted attempts at load testing yield “results”, but too often those results are inaccurate, leading to a false sense of security for anyone who trusts them. This, in turn, leads to the release of applications that are not adequately tested and that experience performance problems soon after entering production.

I was reminded of this not long ago, when I worked with a customer who related an experience that may sound familiar to many of you. This customer was a test engineer for a bank that had recently merged with another bank, effectively doubling their customer base. He was part of a team responsible for load testing a new web application that would serve customers from both of the original banks. Before the application was rolled out, they performed load tests and confirmed that the application could handle the expected number of users with acceptable response times. When the system went live, however, it was slow as molasses — even under user loads less than what the team had tested.

The problem, as you may have guessed, was that the team had not accurately modeled the load. The virtual users used in the testing were a homogenous group that interacted with the system in roughly the same way, from roughly the same geographic locations, at the same network speed. In reality, the customers who came from Bank A tended to perform certain transactions much more frequently than those who came from Bank B. Most of Bank B’s customers lived a different part of country than those from Bank A. More importantly, customers from both banks were accessing the application at widely differing connection speeds across a range of browsers. None of these factors were modeled accurately in the load tests the team had performed. In some cases it was because the team simply had not considered them, in others it was because the load testing tool they were using provided no way to handle these differences. In either case, the result was the same; the team had given the “go live” signal to an application that was not ready, basing their decision on inaccurate load test results.

Too often, organizations take a short cut to load testing. They are focused on a single number: how many concurrent users their application will support. As a result they put little effort into script development, and they end up with an unrealistic test – one of little value. I encourage all load testers to think beyond the concurrent users metric and take a closer look at other factors that go into creating a realistic load that will yield more accurate results, including:

modeling user activity,

modeling different connection speeds,

modeling different browsers and mobile devices,

modeling geographically distributed users.

Parameterizing Scripts to Better Model User Activity

Scripts that simply record a typical user’s interaction with a web application and then play it back are not going to yield accurate performance data. As an example, a script that emulates a user logging into a site, searching for a product, placing it in the cart, and checking out does little to test the performance of other user activities such as checking product reviews, accessing detailed specifications, or comparing products.

More importantly, if the script always logs in as the same user and orders the same product, caching effects will often skew the performance measurements, making response times shorter than they would be under a real-world load. Caching on the web server, application server, and database server all come into play, compounding any caching that is done on the client side.

To minimize caching and similar effects, scripts must be parameterized. In my example above, the script would play back different users searching for different products, and purchasing them via different methods. Ideally the script would use randomization or data customization to fill in every user editable or selectable element on each form of the web application. This script parameterization, combined with creating multiple scripts to address a variety of user interactions, produces a much more realistic user load, and it’s a good idea to have a load testing tool that simplifies these tasks.

Generating a Load With a Mixture of Connection Speeds and Network Characteristics

Many testing teams use the fastest available network connections when load testing a server. The belief is that if the application performs well under those connections, it will be guaranteed to work well in production when many real-world users will have slower connections. This is a faulty assumption that leads to performance problems when the application is subjected to real-world users accessing it at a variety of network bandwidths.

Testing with only high-speed connections can mask performance problems that occur only when lower speed connections are used. Slower data speeds will require connections to the server to stay open longer, and eventually the server may reach its limit for the maximum number of open connections.

Of course, testing with only low-speed connections is equally problematic. What’s needed is a reasonable mixture of virtual users accessing the server at connection speeds representative of everything from 56K modems for dial-up users to T3 lines.

With more and more users accessing the web via mobile devices, it makes sense to include 3G and 4G connection rates in the mix as well. Just as importantly it’s important to take into account disparities in signal strength that can cause packet loss and increased network latency. Built-in support for incorporating these factors in performance testing is increasingly important, particularly for web applications that serve a high percentage of mobile users.

Emulating Different Browsers and Native Mobile Apps

Interestingly enough (and often surprising to some), not all browsers support the same number of concurrent HTTP connections. This obviously needs to be thought of as well — if a load test models the entire user population accessing a web application with a single browser that supports four connections per server, it neglects the effects of browsers that use twice that number.

This leads to a situation similar to the one that arises with inaccurate modeling of connection speeds — with more concurrent connections, it is not unusual to see slowdowns as a server reaches its limit for simultaneous connections. To minimize these effects, load tests should apply a variety of browser profiles during playback, so that the tests identify the traffic as originating from a realistic mixture of different browsers, including mobile browsers.

Mobile devices, in fact, present a new set of challenges for load testers (see Best Practices for Load Testing Mobile Applications, Part 1 and Part 2), aside from the network connection issues I’ve already covered. Many companies now have a separate mobile version of their site, with content tailored specifically for mobile users. Again, to perform a valid load test on such sites, a test engineer must be able to override the browser identification during playback so that the virtual user appears to be using a mobile browser.

But what about native mobile applications? There is no browser involved, so you’ll need a testing solution that can record, parameterize, and playback the network traffic originating from the mobile device. For some cases this can be done via a proxy, but for some apps this is not an available option. These apps may call for a tunneling approach in which the testing tool acts as a DNS server. Even if you’re not facing this situation today, you may want to see if your testing tool supports this feature so you’re prepared when you do need it.

Generating a Geographically Distributed Load

Unless your end-user community is accessing your application from a single location, initiating tests solely from inside your datacenter is unlikely to represent a realistic load. Such tests fail to take into account the effects of third-party servers and content delivery networks that may sit between your users and your web application.

Using the cloud to generate load as part of your testing can better model a geographically distributed user base, one that may include users from around the world, enabling test engineers to generate realistic, large-scale tests across multiple regions. Cloud testing complements internal, lab-based tests and ideally test scripts from one domain are reused in the other. With separate performance metrics for each geographic region in hand, engineers can see where performance issues are likely to arise on a region-by-region basis.

If users are accessing your web site from all over the world, load testing from the cloud helps you model that reality. When this capability is combined with tests that incorporate parameterized scripts, browser differences, support for mobile apps, and a variety of connection speeds and network effects, you can trust the accuracy of your test results.

[…] team will actually pay attention to. This involves modeling real-world factors in your user loads, including users accessing your website and applications from mobile devices and from lots of differe…. With proper load testing you can be prepared when the unexpected deluge of users hits your site. […]

Very good article! How to get the usage model right has been in mind for long time. My 1st question is after you build a model followed best performance test practice or your own proprietary methods, how do you verify the model is correct? Or how close the model is to prod usage model? I do not think you can match prod usage in load testing 100%, right? What is the cut off line there to be acceptable?

I have seldomly seen that deep thoughts/discussion in performance testing on internet. My another question is what are the quality aspects of performance testing itself. I have been researching them but have not got good answers yet.